Fuzzy Logic Programming in Action with FLOPER
ABSTRACT
During the last years, we have developed the FLOPER platform for providing a practical support to the so-called Multi-Adjoint Logic Programming approach (MALP in brief), which represents an extremely flexible framework into the Fuzzy Logic Programming arena. Nowadays, FLOPER is useful for compiling (to standard Prolog code), executing and debugging (by drawing execution trees) MALP programs, and it is ready for being extended in the near future with powerful transformation and optimization techniques designed in our research group during the recent past. Our last update consists in the integration of a graphical interface for a comfortable interaction with the system which allows, among other capabilities, the use of projects for packing scripts and auxiliary definitions of fuzzy sets/connectives, together with fuzzy programs and their associated lattices modeling truth-degrees beyond the simpler crisp case ﹛true;false﹜.
KEYWORDS
Fuzzy Logic Programming, Language Design and Implementation, New Programming Concepts and Paradigms, Software Tools, Cost Measures
Fuzzy Logic Programming, Language Design and Implementation, New Programming Concepts and Paradigms, Software Tools, Cost Measures
Cite this paper
Moreno, G. and Vázquez, C. (2014) Fuzzy Logic Programming in Action with FLOPER. Journal of Software Engineering and Applications, 7, 273-298. doi: 10.4236/jsea.2014.74028.
Moreno, G. and Vázquez, C. (2014) Fuzzy Logic Programming in Action with FLOPER. Journal of Software Engineering and Applications, 7, 273-298. doi: 10.4236/jsea.2014.74028.
References
[1] Lloyd, J. (1987) Foundations of Logic Programming. 2nd Edition, Springer-Verlag, Berlin. [2] Lee, R. (1972) Fuzzy Logic and the Resolution Principle. Journal of the ACM, 19, 119-129. http://dx.doi.org/10.1145/321679.321688 [3] Ishizuka, M. and Kanai, N. (1985) Prolog-ELF Incorporating Fuzzy Logic. Proceedings of the 9th International Joint Conference on Artificial Intelligence, IJCAI’85, Los Angeles, 18-23 August 1985, 701-703. [4] Li, D. and Liu, D. (1990) A Fuzzy Prolog Database System. John Wiley & Sons, Inc., Hoboken. [5] Baldwin, J.F., Martin, T.P., and Pilsworth, B.W. (1995) Fril-Fuzzy and Evidential Reasoning in Artificial Intelligence. ohn Wiley & Sons, Inc., Hoboken. [6] Rodrguez-Artalejo, M. and Romero-Daz, C. (2008) Quantitative Logic Programming Revisited. In: Garrigue, J. and Hermenegildo, M., Eds., Functional and Logic Programming (FLOPS’08), Springer, Berlin, 272-288. [7] Caballero, R., Rodrguez, M., and Romero, C.A. (2010) A Transformation-Based Implementation for CLP with Qualification and Proximity. CoRR, abs/1009.1976. [8] Munoz-Hernandez, S., Pablos-Ceruelo, V. and Strass, H. (2011) Rfuzzy: Syntax, Semantics and Implementation Details of a Simple and Expressive Fuzzy Tool over Prolog. Information Sciences, 181, 1951-1970. http://dx.doi.org/10.1016/j.ins.2010.07.033 [9] Medina, J., Ojeda-Aciego, M. and Vojtás, P. (2004) Similarity-Based Unification: A Multi-Adjoint Approach. Fuzzy Sets and Systems, 146, 43-62. http://dx.doi.org/10.1016/j.fss.2003.11.005 [10] Medina, J., Ojeda-Aciego, M. and Vojtas, P. (2001) Multi-Adjoint Logic Programming with Continuous Semantics. Proceedings of Logic Programming and Non-Monotonic Reasoning, LPNMR’01. In: Lecture Notes in Computer Science, Vol. 2173, Springer-Verlag, Berlin, 351-364. [11] Medina, J., Ojeda-Aciego, M. and Vojtas, P. (2001) A Procedural Semantics for Multi-Adjoint Logic Programing. Progress in Artificial Intelligence, EPIA’01. In: Lecture Notes in Computer Science, Vol. 2258, Springer-Verlag, Berlin, 290-297. [12] Julián, P., Moreno, G. and Penabad, J. (2009) On the Declarative Semantics of Multi-Adjoint Logic Programs. Proceedings of 10th International Work-Conference on Artificial Neural Networks, IWANN’09. In: Lecture Notes in Computer Science, Vol. 5517, Springer-Verlag, Berlin, 253-260. [13] Lassez, J.L., Maher, M.J. and Marriott, K. (1988) Unification Revisited. In: Minker, J., Ed., Foundations of Deductive Databases and Logic Programming, Morgan Kaufmann, Los Altos, 587-625. [14] Julián, P., Moreno, G. and Penabad, J. (2006) Operational/Interpretive Unfolding of Multi-Adjoint Logic Programs. Journal of Universal Computer Science, 12, 1679-1699. [15] Julián, P., Moreno, G. and Penabad, J. (2007) Measuring the Interpretive Cost in Fuzzy Logic Computations. Applications of Fuzzy Sets Theory, Proceedings of 7th International Workshop on Fuzzy Logic and Applications, WILF’07. In: Lecture Notes in Computer Science, Vol. 4578, Springer-Verlag, Berlin, 28-36. [16] Morcillo, P. and Moreno, G. (2009) On Cost Estimations for Executing Fuzzy Logic Programs. Proceedings of the 2009 International Joint Conference on Artificial Intelligence, ICAI’09, Las Vegas, 13-16 July 2009, 217-223. [17] Morcillo, P. and Moreno, G. (2009) Modeling Interpretive Steps in Fuzzy Logic Computations. Proceedings of the 8th International Workshop on Fuzzy Logic and Applications, WILF’09. In: Lecture Notes in Computer Science, Vol. 5571, Springer-Verlag, Berlin, 44-51. [18] Julián, P., Moreno, G. and Penabad, J. (2005) On Fuzzy Unfolding. A Multi-Adjoint Approach. Fuzzy Sets and Systems, 154, 16-33. http://dx.doi.org/10.1016/j.fss.2005.03.013 [19] Guerrero, J. and Moreno, G. (2008) Optimizing Fuzzy Logic Programs by Unfolding, Aggregation and Folding. Electronic Notes in Theoretical Computer Science, 219, 19-34.
http://dx.doi.org/10.1016/j.entcs.2008.10.032 [20] Morcillo, P. and Moreno, G. (2008) Programming with Fuzzy Logic Rules by using the FLOPER Tool. Proceedings of the 2nd Rule Representation, Interchange and Reasoning on the Web, International Symposium, RuleML’08. In: Lecture Notes in Computer Science, Vol. 3521, Springer-Verlag, Berlin, 119-126. [21] Morcillo, P., Moreno, G., Penabad, J. and Vázquez, C. (2010) Modeling Interpretive Steps into the FLOPER Environment. Proceedings of the 2010 International Conference on Artificial Intelligence, ICAI’10, Las Vegas, 12-15 July 2010, 16-22. [22] Morcillo, P., Moreno, G., Penabad, J. and Vázquez, C. (2010) A Practical Management of Fuzzy Truth Degrees Using FLOPER. Proceedings of 4th International Symposium on Rule Interchange and Applications, RuleML’10. In: Lecture Notes in Computer Science, Vol. 6403, Springer-Verlag, Berlin, 20-34. [23] Morcillo, P., Moreno, G., Penabad, J. and Vázquez, C. (2011) Declarative Traces into Fuzzy Computed Answers. Proceedings of 5th International Symposium on Rules: Research Based, Industry Focused, RuleML’11. In: Lecture Notes in Computer Science, Vol. 6826, Springer-Verlag, Berlin, 170-185. [24] Morcillo, P.J., Moreno, G., Penabad, J. and Vázquez, C. (2012) Dedekind-Macneille Completion and Cartesian Product of Multi-Adjoint Lattices. International Journal of Computer Mathematics, 89, 1742-1752. http://dx.doi.org/10.1080/00207160.2012.689826 [25] Morcillo, P., Moreno, G., Penabad, J. and Vázquez, C. (2012) String-Based Multi-Adjoint Lattices for Tracing Fuzzy Logic Computations. Electronic Communications of the EASST, 55, 17. [26] Vaucheret, C., Guadarrama, S. and Mu?oz, S. (2002) Fuzzy Prolog: A Simple General Implementation Using clp(r). Proceedings of Logic for Programming, Artificial Intelligence and Reasoning, LPAR 2002. In: Lecture Notes in Computer Science, Vol. 2514, Springer-Verlag, Berlin, 450-463. [27] Rodrguez, M. and Romero, C.A. (2010) A Declarative Semantics for CLP with Qualification and Proximity. Theory and Practice of Logic Programming, 10, 627-642.
http://dx.doi.org/10.1017/S1471068410000323 [28] Nguyen, H. and Walker, E. (2000) A First Course in Fuzzy Logic. Chapman & Hall/CRC, Boca Ratón. [29] Almendros-Jiménez, J., Luna, A. and Moreno, G. (2012) Fuzzy Logic Programming for Implementing a Flexible XPath-Based Query Language. Electronic Notes on Theoretical Computer Science, ENTCS, 282, 3-18. http://dx.doi.org/10.1016/j.entcs.2011.12.002 [30] Almendros-Jiménez, J., Luna, A. and Moreno, G. (2013) Annotating “Fuzzy Chance Degrees” When Debugging Xpath Queries. Advances in Computational Intelligence-Proceedings of the 12th International Work-Conference on Artificial Neural Networks, IWANN 2013 (Special Session on Fuzzy Logic and Soft Computing Application). In: Lecture Notes in Computer Science, Vol. 7903, Springer-Verlag, Berlin, 300-311. [31] Almendros-Jiménez, J., Luna, A., Moreno, G. and Vázquez, C. (2013) Analyzing Fuzzy Logic Computations with Fuzzy XPath. Actas de las XIII Jornadas sobre Programación y Lenguajes, PROLE’13, Jornadas SISTEDES, Madrid, 18-20 September 2013, 136-150. [32] Bofill, M., Moreno, G., Vázquez, C. and Villaret, M. (2013) Automatic Proving of Fuzzy Formulae with Fuzzy Logic Programming and SMT. Actas de las XIII Jornadas sobre Programación y Lenguajes, PROLE’13, Jornadas SISTEDES, Madrid, 18-20 September 2013, 151-165. [33] Vázquez, C., Tomás, L., Moreno, G. and Tordsson, J. (2013) A Fuzzy Approach to Cloud Admission Control for Safe overbooking. Proceedings of 10th International Workshop on Fuzzy Logic and Applications, WILF 2013, Genoa, 7-10 November 2013, 212-225. [34] Julián, P., Medina, J., Moreno, G. and Ojeda, M. (2010) Efficient Thresholded Tabulation for Fuzzy Query Answering. Studies in Fuzziness and Soft Computing (Foundations of Reasoning under Uncertainty), 249, 125-141. http://dx.doi.org/10.1007/978-3-642-10728-3_7 [35] Julián, P., Medina, J., Morcillo, P., Moreno, G. and Ojeda-Aciego, M. (2013) An Unfolding-Based Preprocess for Reinforcing Thresholds in Fuzzy Tabulation. In: I. Rojas, G. J. Caparrós, and J. Cabestany, Eds., IWANN (1). In: Lecture Notes in Computer Science, Vol. 7902, Springer, Berlin, 647-655. [36] Bruno, N., Chaudhuri, S. and Gravano, L. (2002) Top-K Selection Queries over Relational Databases: Mapping Strategies and Performance Evaluation. ACM Transactions on Database Systems, 27, 153-187. http://dx.doi.org/10.1145/568518.568519 [37] Chang, K.C.C. and Hwang, S.W. (2002) Minimal Probing: Supporting Expensive Predicates for Top-K Queries. SIGMOD Proceedings of the 2002 ACM SIGMOD International Conference on Management of DataMadison, 3-6 June 2002, 346-357. [38] Ilyas, I.F., Aref, W.G. and Elmagarmid, A.K. (2004) Supporting Top-K Join Queries in Relational Databases. The VLDB Journal, 13, 207-221. http://dx.doi.org/10.1007/s00778-004-0128-2 [39] Chaudhuri, S., Gravano, L. and Marian, A. (2004) Optimizing Top-K Selection Queries over Multimedia Repositories. IEEE Transactions on Knowledge and Data Engineering, 16, 992-1009.
http://dx.doi.org/10.1109/TKDE.2004.30 [40] Marian, A., Bruno, N. and Gravano, L. (2004) Evaluating Top-K Queries over Web-Accessible Databases. ACM Transactions on Database Systems, 29, 319-362. http://dx.doi.org/10.1145/ 1005566.1005569 [41] Theobald, M., Schenkel, R. and Weikum, G. (2005) Efficient and Self-Tuning Incremental Query Expansion for Top-K Query Processing. Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Salvador, 15-19 August 2005, 242-249. [42] Li, C., Chang, K.C.C., Ilyas, I.F. and Song, S. (2005) Ranksql: Query Algebra and Optimization for Relational Top-K Queries. SIGMOD Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, Baltimore, 13-17 June 2005, 131-142. [43] Lukasiewicz, T. and Straccia, U. (2007) Top-K Retrieval in Description Logic Programs under Vagueness for the Semantic Web. In: Prade, H. and Subrahmanian, V.S., Eds., SUM, In: Lecture Notes in Computer Science, Vol. 4772. Springer, Berlin, 16-30. [44] Re, C., Dalvi, N.N. and Suciu, D. (2007) Efficient Top-K Query Evaluation on Probabilistic Data. IEEE 23rd International Conference on Data Engineering, Istanbul, 15-20 April 2007, 886-895. [45] Eckhardt, A., Horváth, T. and Vojtás, P. (2007) Learning Different User Profile Annotated Rules for Fuzzy Preference Top-K Querying. In: Prade, H. and Subrahmanian, V.S., Eds., SUM, In: Lecture Notes in Computer Science, Vol. 4772. Springer, Berlin, 116-130. [46] Ilyas, I.F., Beskales, G. and Soliman, M.A. (2008) A Survey of Top-K Query Processing Techniques in Relational Database Systems. ACM Computing Surveys, 40, Article No. 11. [47] Prade, H. and Subrahmanian, V.S. (Eds.) (2007) Scalable Uncertainty Management. Proceedings of the 1st International Conference, SUM 2007, Washington DC, 10-12 October 2007. In: Lecture Notes in Computer Science, Vol. 4772, Springer, Berlin.
[1] Lloyd, J. (1987) Foundations of Logic Programming. 2nd Edition, Springer-Verlag, Berlin. [2] Lee, R. (1972) Fuzzy Logic and the Resolution Principle. Journal of the ACM, 19, 119-129. http://dx.doi.org/10.1145/321679.321688 [3] Ishizuka, M. and Kanai, N. (1985) Prolog-ELF Incorporating Fuzzy Logic. Proceedings of the 9th International Joint Conference on Artificial Intelligence, IJCAI’85, Los Angeles, 18-23 August 1985, 701-703. [4] Li, D. and Liu, D. (1990) A Fuzzy Prolog Database System. John Wiley & Sons, Inc., Hoboken. [5] Baldwin, J.F., Martin, T.P., and Pilsworth, B.W. (1995) Fril-Fuzzy and Evidential Reasoning in Artificial Intelligence. ohn Wiley & Sons, Inc., Hoboken. [6] Rodrguez-Artalejo, M. and Romero-Daz, C. (2008) Quantitative Logic Programming Revisited. In: Garrigue, J. and Hermenegildo, M., Eds., Functional and Logic Programming (FLOPS’08), Springer, Berlin, 272-288. [7] Caballero, R., Rodrguez, M., and Romero, C.A. (2010) A Transformation-Based Implementation for CLP with Qualification and Proximity. CoRR, abs/1009.1976. [8] Munoz-Hernandez, S., Pablos-Ceruelo, V. and Strass, H. (2011) Rfuzzy: Syntax, Semantics and Implementation Details of a Simple and Expressive Fuzzy Tool over Prolog. Information Sciences, 181, 1951-1970. http://dx.doi.org/10.1016/j.ins.2010.07.033 [9] Medina, J., Ojeda-Aciego, M. and Vojtás, P. (2004) Similarity-Based Unification: A Multi-Adjoint Approach. Fuzzy Sets and Systems, 146, 43-62. http://dx.doi.org/10.1016/j.fss.2003.11.005 [10] Medina, J., Ojeda-Aciego, M. and Vojtas, P. (2001) Multi-Adjoint Logic Programming with Continuous Semantics. Proceedings of Logic Programming and Non-Monotonic Reasoning, LPNMR’01. In: Lecture Notes in Computer Science, Vol. 2173, Springer-Verlag, Berlin, 351-364. [11] Medina, J., Ojeda-Aciego, M. and Vojtas, P. (2001) A Procedural Semantics for Multi-Adjoint Logic Programing. Progress in Artificial Intelligence, EPIA’01. In: Lecture Notes in Computer Science, Vol. 2258, Springer-Verlag, Berlin, 290-297. [12] Julián, P., Moreno, G. and Penabad, J. (2009) On the Declarative Semantics of Multi-Adjoint Logic Programs. Proceedings of 10th International Work-Conference on Artificial Neural Networks, IWANN’09. In: Lecture Notes in Computer Science, Vol. 5517, Springer-Verlag, Berlin, 253-260. [13] Lassez, J.L., Maher, M.J. and Marriott, K. (1988) Unification Revisited. In: Minker, J., Ed., Foundations of Deductive Databases and Logic Programming, Morgan Kaufmann, Los Altos, 587-625. [14] Julián, P., Moreno, G. and Penabad, J. (2006) Operational/Interpretive Unfolding of Multi-Adjoint Logic Programs. Journal of Universal Computer Science, 12, 1679-1699. [15] Julián, P., Moreno, G. and Penabad, J. (2007) Measuring the Interpretive Cost in Fuzzy Logic Computations. Applications of Fuzzy Sets Theory, Proceedings of 7th International Workshop on Fuzzy Logic and Applications, WILF’07. In: Lecture Notes in Computer Science, Vol. 4578, Springer-Verlag, Berlin, 28-36. [16] Morcillo, P. and Moreno, G. (2009) On Cost Estimations for Executing Fuzzy Logic Programs. Proceedings of the 2009 International Joint Conference on Artificial Intelligence, ICAI’09, Las Vegas, 13-16 July 2009, 217-223. [17] Morcillo, P. and Moreno, G. (2009) Modeling Interpretive Steps in Fuzzy Logic Computations. Proceedings of the 8th International Workshop on Fuzzy Logic and Applications, WILF’09. In: Lecture Notes in Computer Science, Vol. 5571, Springer-Verlag, Berlin, 44-51. [18] Julián, P., Moreno, G. and Penabad, J. (2005) On Fuzzy Unfolding. A Multi-Adjoint Approach. Fuzzy Sets and Systems, 154, 16-33. http://dx.doi.org/10.1016/j.fss.2005.03.013 [19] Guerrero, J. and Moreno, G. (2008) Optimizing Fuzzy Logic Programs by Unfolding, Aggregation and Folding. Electronic Notes in Theoretical Computer Science, 219, 19-34.
http://dx.doi.org/10.1016/j.entcs.2008.10.032 [20] Morcillo, P. and Moreno, G. (2008) Programming with Fuzzy Logic Rules by using the FLOPER Tool. Proceedings of the 2nd Rule Representation, Interchange and Reasoning on the Web, International Symposium, RuleML’08. In: Lecture Notes in Computer Science, Vol. 3521, Springer-Verlag, Berlin, 119-126. [21] Morcillo, P., Moreno, G., Penabad, J. and Vázquez, C. (2010) Modeling Interpretive Steps into the FLOPER Environment. Proceedings of the 2010 International Conference on Artificial Intelligence, ICAI’10, Las Vegas, 12-15 July 2010, 16-22. [22] Morcillo, P., Moreno, G., Penabad, J. and Vázquez, C. (2010) A Practical Management of Fuzzy Truth Degrees Using FLOPER. Proceedings of 4th International Symposium on Rule Interchange and Applications, RuleML’10. In: Lecture Notes in Computer Science, Vol. 6403, Springer-Verlag, Berlin, 20-34. [23] Morcillo, P., Moreno, G., Penabad, J. and Vázquez, C. (2011) Declarative Traces into Fuzzy Computed Answers. Proceedings of 5th International Symposium on Rules: Research Based, Industry Focused, RuleML’11. In: Lecture Notes in Computer Science, Vol. 6826, Springer-Verlag, Berlin, 170-185. [24] Morcillo, P.J., Moreno, G., Penabad, J. and Vázquez, C. (2012) Dedekind-Macneille Completion and Cartesian Product of Multi-Adjoint Lattices. International Journal of Computer Mathematics, 89, 1742-1752. http://dx.doi.org/10.1080/00207160.2012.689826 [25] Morcillo, P., Moreno, G., Penabad, J. and Vázquez, C. (2012) String-Based Multi-Adjoint Lattices for Tracing Fuzzy Logic Computations. Electronic Communications of the EASST, 55, 17. [26] Vaucheret, C., Guadarrama, S. and Mu?oz, S. (2002) Fuzzy Prolog: A Simple General Implementation Using clp(r). Proceedings of Logic for Programming, Artificial Intelligence and Reasoning, LPAR 2002. In: Lecture Notes in Computer Science, Vol. 2514, Springer-Verlag, Berlin, 450-463. [27] Rodrguez, M. and Romero, C.A. (2010) A Declarative Semantics for CLP with Qualification and Proximity. Theory and Practice of Logic Programming, 10, 627-642.
http://dx.doi.org/10.1017/S1471068410000323 [28] Nguyen, H. and Walker, E. (2000) A First Course in Fuzzy Logic. Chapman & Hall/CRC, Boca Ratón. [29] Almendros-Jiménez, J., Luna, A. and Moreno, G. (2012) Fuzzy Logic Programming for Implementing a Flexible XPath-Based Query Language. Electronic Notes on Theoretical Computer Science, ENTCS, 282, 3-18. http://dx.doi.org/10.1016/j.entcs.2011.12.002 [30] Almendros-Jiménez, J., Luna, A. and Moreno, G. (2013) Annotating “Fuzzy Chance Degrees” When Debugging Xpath Queries. Advances in Computational Intelligence-Proceedings of the 12th International Work-Conference on Artificial Neural Networks, IWANN 2013 (Special Session on Fuzzy Logic and Soft Computing Application). In: Lecture Notes in Computer Science, Vol. 7903, Springer-Verlag, Berlin, 300-311. [31] Almendros-Jiménez, J., Luna, A., Moreno, G. and Vázquez, C. (2013) Analyzing Fuzzy Logic Computations with Fuzzy XPath. Actas de las XIII Jornadas sobre Programación y Lenguajes, PROLE’13, Jornadas SISTEDES, Madrid, 18-20 September 2013, 136-150. [32] Bofill, M., Moreno, G., Vázquez, C. and Villaret, M. (2013) Automatic Proving of Fuzzy Formulae with Fuzzy Logic Programming and SMT. Actas de las XIII Jornadas sobre Programación y Lenguajes, PROLE’13, Jornadas SISTEDES, Madrid, 18-20 September 2013, 151-165. [33] Vázquez, C., Tomás, L., Moreno, G. and Tordsson, J. (2013) A Fuzzy Approach to Cloud Admission Control for Safe overbooking. Proceedings of 10th International Workshop on Fuzzy Logic and Applications, WILF 2013, Genoa, 7-10 November 2013, 212-225. [34] Julián, P., Medina, J., Moreno, G. and Ojeda, M. (2010) Efficient Thresholded Tabulation for Fuzzy Query Answering. Studies in Fuzziness and Soft Computing (Foundations of Reasoning under Uncertainty), 249, 125-141. http://dx.doi.org/10.1007/978-3-642-10728-3_7 [35] Julián, P., Medina, J., Morcillo, P., Moreno, G. and Ojeda-Aciego, M. (2013) An Unfolding-Based Preprocess for Reinforcing Thresholds in Fuzzy Tabulation. In: I. Rojas, G. J. Caparrós, and J. Cabestany, Eds., IWANN (1). In: Lecture Notes in Computer Science, Vol. 7902, Springer, Berlin, 647-655. [36] Bruno, N., Chaudhuri, S. and Gravano, L. (2002) Top-K Selection Queries over Relational Databases: Mapping Strategies and Performance Evaluation. ACM Transactions on Database Systems, 27, 153-187. http://dx.doi.org/10.1145/568518.568519 [37] Chang, K.C.C. and Hwang, S.W. (2002) Minimal Probing: Supporting Expensive Predicates for Top-K Queries. SIGMOD Proceedings of the 2002 ACM SIGMOD International Conference on Management of DataMadison, 3-6 June 2002, 346-357. [38] Ilyas, I.F., Aref, W.G. and Elmagarmid, A.K. (2004) Supporting Top-K Join Queries in Relational Databases. The VLDB Journal, 13, 207-221. http://dx.doi.org/10.1007/s00778-004-0128-2 [39] Chaudhuri, S., Gravano, L. and Marian, A. (2004) Optimizing Top-K Selection Queries over Multimedia Repositories. IEEE Transactions on Knowledge and Data Engineering, 16, 992-1009.
http://dx.doi.org/10.1109/TKDE.2004.30 [40] Marian, A., Bruno, N. and Gravano, L. (2004) Evaluating Top-K Queries over Web-Accessible Databases. ACM Transactions on Database Systems, 29, 319-362. http://dx.doi.org/10.1145/ 1005566.1005569 [41] Theobald, M., Schenkel, R. and Weikum, G. (2005) Efficient and Self-Tuning Incremental Query Expansion for Top-K Query Processing. Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Salvador, 15-19 August 2005, 242-249. [42] Li, C., Chang, K.C.C., Ilyas, I.F. and Song, S. (2005) Ranksql: Query Algebra and Optimization for Relational Top-K Queries. SIGMOD Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, Baltimore, 13-17 June 2005, 131-142. [43] Lukasiewicz, T. and Straccia, U. (2007) Top-K Retrieval in Description Logic Programs under Vagueness for the Semantic Web. In: Prade, H. and Subrahmanian, V.S., Eds., SUM, In: Lecture Notes in Computer Science, Vol. 4772. Springer, Berlin, 16-30. [44] Re, C., Dalvi, N.N. and Suciu, D. (2007) Efficient Top-K Query Evaluation on Probabilistic Data. IEEE 23rd International Conference on Data Engineering, Istanbul, 15-20 April 2007, 886-895. [45] Eckhardt, A., Horváth, T. and Vojtás, P. (2007) Learning Different User Profile Annotated Rules for Fuzzy Preference Top-K Querying. In: Prade, H. and Subrahmanian, V.S., Eds., SUM, In: Lecture Notes in Computer Science, Vol. 4772. Springer, Berlin, 116-130. [46] Ilyas, I.F., Beskales, G. and Soliman, M.A. (2008) A Survey of Top-K Query Processing Techniques in Relational Database Systems. ACM Computing Surveys, 40, Article No. 11. [47] Prade, H. and Subrahmanian, V.S. (Eds.) (2007) Scalable Uncertainty Management. Proceedings of the 1st International Conference, SUM 2007, Washington DC, 10-12 October 2007. In: Lecture Notes in Computer Science, Vol. 4772, Springer, Berlin.